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- Publications
- Influence
Greedy function approximation: A gradient boosting machine.
- J. Friedman
- Mathematics
- 1 October 2001
Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions… Expand
The Elements of Statistical Learning
- T. Hastie, R. Tibshirani, J. Friedman
- Computer Science
- 2001
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
- T. Hastie, R. Tibshirani, J. Friedman
- Mathematics, Computer Science
- Springer Series in Statistics
- 1 March 2005
TLDR
Regularization Paths for Generalized Linear Models via Coordinate Descent.
- J. Friedman, T. Hastie, R. Tibshirani
- Computer Science, Medicine
- Journal of statistical software
- 2 February 2010
TLDR
Sparse inverse covariance estimation with the graphical lasso.
- J. Friedman, T. Hastie, R. Tibshirani
- Mathematics, Medicine
- Biostatistics
- 1 July 2008
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the… Expand
Stochastic gradient boosting
- J. Friedman
- Mathematics
- 28 February 2002
Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo'-residuals by least squares at each iteration. The… Expand
Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By
The main and important contribution of this paper is in establishing a connection between boosting, a newcomer to the statistics scene, and additive models. One of the main properties of boosting… Expand
- 1,622
- 327
Regularized Discriminant Analysis
- J. Friedman
- Mathematics
- 1 March 1989
Abstract Linear and quadratic discriminant analysis are considered in the small-sample, high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance… Expand
Special Invited Paper-Additive logistic regression: A statistical view of boosting
- J. Friedman
- Mathematics
- 1 April 2000
Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data… Expand
PATHWISE COORDINATE OPTIMIZATION
- J. Friedman, T. Hastie, Holger Hofling, R. Tibshirani
- Mathematics
- 10 August 2007
We consider “one-at-a-time” coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the L1-penalized regression (lasso) in the… Expand